Cross-validation of an artificial intelligence tool for fracture classification and localization on conventional radiography in Dutch population Article Swipe
Huibert C. Ruitenbeek
,
Sahil Sahil
,
Anil Kumar
,
Ravi Kumar Kushawaha
,
Swetha Tanamala
,
Saigopal Sathyamurthy
,
Rohitashva Agrawal
,
Subhankar Chattoraj
,
Jasika Paramasamy
,
Daniël Bos
,
Roshan Fahimi
,
Edwin H. G. Oei
,
Jacob J. Visser
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s13244-025-02034-1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1186/s13244-025-02034-1
Objectives The aim of this study is to validate the effectiveness of an AI tool trained on Indian data in a Dutch medical center and to assess its ability to classify and localize fractures. Methods Conventional radiographs acquired between January 2019 and November 2022 were analyzed using a multitask deep neural network. The tool, trained on Indian data, identified and localized fractures in 17 body parts. The reference standard was based on radiology reports resulting from routine clinical workflow and confirmed by an experienced musculoskeletal radiologist. The analysis included both patient-wise and fracture-wise evaluations, employing binary and Intersection over Union (IoU) metrics to assess fracture detection and localization accuracy. Results In total, 14,311 radiographs (median age, 48 years (range 18–98), 7265 male) were analyzed and categorized by body parts; clavicle, shoulder, humerus, elbow, forearm, wrist, hand and finger, pelvis, hip, femur, knee, lower leg, ankle, foot and toe. 4156/14,311 (29%) had fractures. The AI tool demonstrated overall patient-wise sensitivity, specificity, and AUC of 87.1% (95% CI: 86.1–88.1%), 87.1% (95% CI: 86.4–87.7%), and 0.92 (95% CI: 0.91–0.93), respectively. Fracture detection rate was 60% overall, ranging from 7% for rib fractures to 90% for clavicle fractures. Conclusions This study validates a fracture detection AI tool on a Western-European dataset, originally trained on Indian data. While classification performance is robust on real clinical data, fracture-wise analysis reveals variability in localization accuracy, underscoring the need for refinement in fracture localization. Critical relevance statement AI may provide help by enabling optimal use of limited resources or personnel. This study evaluates an AI tool designed to aid in detecting fractures, possibly reducing reading time or optimization of radiology workflow by prioritizing fracture-positive cases. Key Points Cross-validation on a consecutive Dutch cohort confirms this AI tool’s clinical robustness. The tool detected fractures with 87% sensitivity, 87% specificity, and 0.92 AUC. AI localizes 60% of fractures, the highest for clavicle (90%) and lowest for ribs (7%). Graphical Abstract
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s13244-025-02034-1
- https://insightsimaging.springeropen.com/counter/pdf/10.1186/s13244-025-02034-1
- OA Status
- gold
- Cited By
- 1
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411983169
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4411983169Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s13244-025-02034-1Digital Object Identifier
- Title
-
Cross-validation of an artificial intelligence tool for fracture classification and localization on conventional radiography in Dutch populationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-03Full publication date if available
- Authors
-
Huibert C. Ruitenbeek, Sahil Sahil, Anil Kumar, Ravi Kumar Kushawaha, Swetha Tanamala, Saigopal Sathyamurthy, Rohitashva Agrawal, Subhankar Chattoraj, Jasika Paramasamy, Daniël Bos, Roshan Fahimi, Edwin H. G. Oei, Jacob J. VisserList of authors in order
- Landing page
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https://doi.org/10.1186/s13244-025-02034-1Publisher landing page
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https://insightsimaging.springeropen.com/counter/pdf/10.1186/s13244-025-02034-1Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://insightsimaging.springeropen.com/counter/pdf/10.1186/s13244-025-02034-1Direct OA link when available
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Medicine, Radiography, Periprosthetic, Wrist, Pelvis, Radiology, Humerus, Forearm, Elbow, Artificial intelligence, Arthroplasty, Surgery, Computer scienceTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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26Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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